CN113670428B - Transformer vibration online abnormality detection method - Google Patents
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- CN113670428B CN113670428B CN202110811692.8A CN202110811692A CN113670428B CN 113670428 B CN113670428 B CN 113670428B CN 202110811692 A CN202110811692 A CN 202110811692A CN 113670428 B CN113670428 B CN 113670428B
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- 230000005856 abnormality Effects 0.000 title claims abstract description 18
- 238000001514 detection method Methods 0.000 title abstract description 12
- 238000000034 method Methods 0.000 claims abstract description 30
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000005469 granulation Methods 0.000 claims 1
- 230000003179 granulation Effects 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 3
- 238000004445 quantitative analysis Methods 0.000 abstract description 3
- 238000004804 winding Methods 0.000 description 8
- 238000005070 sampling Methods 0.000 description 7
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000001351 cycling effect Effects 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
Abstract
Aiming at how to perform refined analysis and anomaly detection on the running state of a transformer based on a vibration method at present, the embodiment of the invention discloses a method for detecting the on-line anomaly of the vibration of the transformer. Therefore, the method divides the operation working condition of the transformer according to the load current, analyzes and judges the current operation state of the transformer based on the historical data under different working conditions, and realizes quantitative analysis of the vibration signal of the transformer under the consideration of working conditions, so that the method for detecting the abnormality of the transformer is more rigorous.
Description
Technical Field
The invention relates to the field of electric power, in particular to a transformer vibration online abnormality detection method.
Background
Power transformers are important devices in power systems, the operating state of which has a significant impact on the safety and economic efficiency of the power system. The surface vibration of the transformer mainly originates from the vibration of the winding and the iron core under the excitation of current and voltage, and theoretical analysis and practical experience show that the working states of the winding and the iron core can be analyzed through the vibration signals of the transformer. When abnormal conditions such as looseness occur in structural components such as iron cores and windings in the transformer, the vibration intensity is increased, the natural frequency components are increased, and the signal complexity is increased. However, in the actual operation process, load current tends to fluctuate greatly, when load current increases, electromotive force born by windings increases in square, so that higher harmonic components in vibration signals are increased, signal complexity increases, and a plurality of problems are caused for abnormality detection of a power transformer based on a vibration analysis method.
Disclosure of Invention
In order to solve the technical problems, the invention provides an online abnormal detection method for vibration of a transformer, which divides operation working condition intervals according to the magnitude of load current, acquires a vibration signal on the surface of the transformer by using an acceleration sensor, judges the working condition interval to which the vibration signal belongs, calculates the complexity of the vibration signals of different load current intervals based on a Lempel-Ziv algorithm, and judges the operation state of the transformer by comparing the complexity threshold value set in the interval to which the vibration signal belongs.
The embodiment of the invention provides the following technical scheme:
a transformer vibration online abnormality detection method comprises the following steps:
dividing the operation working condition interval of the transformer according to the load current;
acquiring a vibration signal of the surface of the transformer by using an acceleration sensor, and judging a working condition interval to which the vibration signal belongs;
calculating the complexity of vibration signals of different load current intervals based on a Lempel-Ziv algorithm;
and comparing the complexity threshold value with the complexity threshold value set in the section to judge the running state of the transformer.
The method for dividing the operation working condition interval of the transformer according to the load current comprises the following steps:
and dividing working condition intervals of the transformer according to the magnitude of the load current. The rated current of the transformer is set as X, and the working condition of the transformer is divided into 5 sections according to the load current value: [0,0.3X), [0.3X, 0.5X), [0.5X, 0.8X), [0.8X, 1X), and [1X, 1.5X), and these 5 intervals are defined as operating conditions A, B, C, D and E.
The method for calculating the complexity of the vibration signal based on the Lempel-Ziv algorithm comprises the following steps:
1) And carrying out binary coarse graining on the vibration signal sampled each time, namely 5k data points, so as to obtain a binary sequence S. Firstly, calculating the average value of an original vibration signal time sequence, and assigning points larger than the average value as 1 and points smaller than the average value as 0 to obtain a binary sequence S= { S 1 ,S 2 ,...,S 5000 }；
2)P 0 、Q 0 Let i=0 for a null matrix, when complexity C (i) =0;
3) And enters a circulation. i=i+1, let P i-1 ＝{P i-1 S i }，Q i-1 ＝{Q i-1 S i ' thereafter judging P i-1 Whether or not to contain Q i . If the judgment result is yes, the complexity C (i) is not increased, namely, C (i) =C (i-1); if the determination result is "no", C (i) =c (i-1) +1, q i = { }. Cycling n=5000 times until the binary sequence S is traversed;
4) Complexity C N Normalization. For the binary sequence S, the final complexity calculation result is normalized, and the calculation method is as follows:
the method for judging the running state of the transformer by comparing the running state with the complexity threshold value set in the section comprises the following steps:
the threshold setting method is as follows:
the complexity threshold value of the vibration signal in the working condition A interval is the complexity average value of the historical vibration data and is marked as C Amean By analogy, the other 4 interval thresholds are noted as C Bmean 、C Cmean 、C Dmean And C Emean 。
The running state of the transformer is divided into four types of normal, attention, alarm and fault, and the specific judging running state rule is as follows:
1) When the absolute value of the difference between the calculated value of C and the belonging section threshold value is greater than or equal to 0.5 times of the belonging section threshold value, the current running state is judged to be attention;
2) When the absolute value of the difference between the calculated value of C and the affiliated section threshold value is larger than the affiliated section threshold value which is 1 time, judging the current running state as an alarm;
3) Within 24 hours, when the absolute value of the difference between the calculated value of C and the affiliated section threshold value is more than 1 time, judging that the current running state is a fault;
4) When any 2 or all of 1), 2) and 3) are satisfied, judging that the current running state is the state with the highest degree of abnormality, and sequentially carrying out normal, attention, alarm and fault on the order of the degree of abnormality from low to high;
5) And when any one of the above 1), 2) and 3) is not satisfied, determining that the current operation state is normal.
Compared with the prior art, the technical scheme has the following advantages:
the inventor of the application considers that load current tends to fluctuate greatly in the running process of the transformer, when the load current increases, the electromotive force born by the winding increases according to square times, so that higher harmonic components in a vibration signal are increased, and the signal complexity is increased; likewise, when abnormal loosening of internal structural components of the transformer, such as the iron core and windings, occurs, higher harmonics in the vibration signal are increased, so that the complexity of the signal is increased. Therefore, the method divides the operation working condition of the transformer according to the load current, analyzes and judges the current operation state of the transformer based on the historical data under different working conditions, and realizes quantitative analysis of the vibration signal of the transformer under the consideration of working conditions, so that the method for detecting the abnormality of the transformer is more rigorous.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a method for detecting online anomalies in transformer vibration;
FIG. 2 is a flow chart for performing a complexity calculation of a vibration signal based on the Lempel-Ziv algorithm.
Detailed Description
As described in the background art, how to accurately determine the operation state of the transformer based on the vibration method is a technical problem that needs to be solved by those skilled in the art.
In order to solve the technical problems, the invention provides an online abnormality detection method for vibration of a transformer, which divides the operation working condition interval of the transformer according to the magnitude of load current, acquires a vibration signal on the surface of the transformer by using an acceleration sensor, judges the working condition interval to which the vibration signal belongs, calculates the complexity of the vibration signals of different load current intervals based on a Lempel-Ziv algorithm, and judges the operation state of the transformer by comparing the complexity with a threshold value set in each load current interval. The method has the advantages that the complexity of the vibration signal of the transformer is quantitatively analyzed by utilizing the Lempel-Ziv algorithm, and the running condition factors of the transformer are considered for analyzing and judging the running state of the transformer, so that the method has stronger practicability and higher detection precision.
Fig. 1 is a schematic diagram of a method for detecting online abnormality of vibration of a transformer, the method comprising:
step 1: and dividing the operation working condition interval of the transformer according to the load current.
The transformer has large load current, heavy load or overload operation, and influences the safety and the reliability and the service life. The invention divides the working condition of the transformer according to the magnitude of the load current. The rated current of the transformer is set as X, and the working condition of the transformer is divided into 5 sections according to the load current value: [0,0.3X), [0.3X, 0.5X), [0.5X, 0.8X), [0.8X, 1X), and [1X, 1.5X), and these 5 intervals are defined as operating conditions A, B, C, D and E.
Step 2: and acquiring a vibration signal of the surface of the transformer by using an acceleration sensor, and judging a working condition interval to which the vibration signal belongs.
And placing the vibration acceleration sensor on a vibration measuring point on the surface of a preset power transformer, setting the sampling rate of the vibration acceleration sensor to be 5kHz, setting the sampling length of each sampling to be 5k, namely obtaining 5k sampling values each time, setting the sampling time length of each sampling to be 1 second, and recording the load current at the same time of each sampling.
Step 3: and (5) completing complexity calculation of the vibration signal based on a Lempel-Ziv algorithm. The computational flow diagram is referred to in fig. 2.
The method for calculating the complexity of the vibration signal based on the Lempel-Ziv algorithm comprises the following steps:
1) The vibration signal, i.e., 5k data points, for each sample was subjected to binary graining. Firstly, calculating the average value of an original vibration signal time sequence, and assigning points larger than the average value as 1 and points smaller than the average value as 0 to obtain a binary sequence S= { S 1 ,S 2 ,...,S 5000 }；
2)P 0 、Q 0 Let i=0 for a null matrix, when complexity C (i) =0;
3) Entering a loop, i=i+1, let P i-1 ＝{P i-1 S i }，Q i-1 ＝{Q i-1 S i ' thereafter judging P i-1 Whether or not to contain Q i If the determination result is yes, the complexity C (i) does not increase, i.e., C (i) =c (i-1). If the determination result is "no", C (i) =c (i-1) +1, q i = { }. Cycling n=5000 times until the binary sequence S is traversed;
4) Complexity C N Normalization. For the binary sequence S, the final complexity calculation result is normalized, and the calculation method is as follows:
step 4: and comparing the complexity threshold value with the complexity threshold value set in the section to judge the running state of the transformer.
The threshold setting method is as follows:
the complexity threshold value of the vibration signal in the working condition A interval is the complexity average value of the historical vibration data and is marked as C Amean By analogy, the other 4 interval thresholds are noted as C Bmean 、C Cmean 、C Dmean And C Emean 。
The running state of the transformer is divided into four types of normal, attention, alarm and fault, and the specific judging running state rule is as follows:
1) When the absolute value of the difference between the calculated value of C and the belonging section threshold value is greater than or equal to 0.5 times of the belonging section threshold value, the current running state is judged to be attention;
2) When the absolute value of the difference between the calculated value of C and the affiliated section threshold value is larger than the affiliated section threshold value which is 1 time, judging the current running state as an alarm;
3) Within 24 hours, when the absolute value of the difference between the calculated value of C and the affiliated section threshold value is more than 1 time, judging that the current running state is a fault;
4) When any 2 or all of 1), 2) and 3) are satisfied, judging that the current running state is the state with the highest degree of abnormality, and sequentially carrying out normal, attention, alarm and fault on the order of the degree of abnormality from low to high;
5) And when any one of the above 1), 2) and 3) is not satisfied, determining that the current operation state is normal.
In summary, the inventor of the application finds that in the actual operation process of the transformer, load current tends to fluctuate greatly, and when the load current increases, the electromotive force born by the winding increases according to square times, so that higher harmonic components in a vibration signal are increased, and the signal complexity is increased; likewise, when abnormal loosening of internal structural components of the transformer, such as the iron core and windings, occurs, higher harmonics in the vibration signal are increased, so that the complexity of the signal is increased. Therefore, the inventors considered that it is necessary to consider an important factor of current change in abnormality detection of the vibration signal. Therefore, the method divides the operation working condition of the transformer according to the load current, analyzes and judges the current operation state of the transformer based on the historical data under different working conditions, and realizes quantitative analysis of the vibration signal of the transformer under the consideration of working conditions, so that the method for detecting the abnormality of the transformer is more rigorous. Specifically, the method utilizes the Lempel-Ziv algorithm to calculate the complexity of the vibration signals of different load current intervals, so that the influence of excessive current change on the vibration signals is inhibited to a certain extent, the vibration signals of the transformer can be more finely quantitatively analyzed, and the erroneous judgment caused by the current change is reduced in the abnormal detection process, so that the detection method is more rigorous.
In the present description, each part is described in a progressive manner, and each part is mainly described as different from other parts, and identical and similar parts between the parts are mutually referred.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (1)
1. The method for detecting the vibration on-line abnormality of the transformer is characterized by comprising the following steps of:
dividing the operation working condition interval of the transformer according to the load current;
acquiring a transformer vibration signal by using an acceleration sensor, and judging a working condition interval to which the vibration signal belongs;
calculating the complexity of vibration signals of different load current intervals based on a Lempel-Ziv algorithm;
judging the running state of the transformer by comparing the running state with a complexity threshold value set in the section to which the running state belongs;
the method for dividing the operation working condition interval of the transformer according to the load current comprises the following steps:
dividing working condition intervals of the transformer according to the magnitude of load current, setting rated current of the transformer as X, and dividing working conditions of the transformer into 5 intervals according to load current values: [0,0.3X), [0.3X, 0.5X), [0.5X, 0.8X), [0.8X, 1X), and [1X, 1.5X), and define these 5 intervals as operating conditions A, B, C, D and E;
the method for calculating the complexity of the vibration signal based on the Lempel-Ziv algorithm comprises the following steps:
1) Performing binary coarse granulation on each sampled vibration signal, namely 5k data points, to obtain a binary sequence S, firstly, calculating the average value of the original vibration signal time sequence, and assigning the point larger than the average value as 1 and the point smaller than the average value as 0 to obtain a binary sequence S= { S 1 ,S 2 ,...,S 5000 }；
2)P 0 、Q 0 Let i=0 for a null matrix, when complexity C (i) =0;
3) Entering a loop, i=i+1, let P i-1 ＝{P i-1 S i }，Q i-1 ＝{Q i-1 S i ' thereafter judging P i-1 Whether or not to contain Q i If the determination result is yes, the complexity C (i) is not increased, i.e., C (i) =c (i-1); if the determination result is "no", C (i) =c (i-1) +1, q i = { } loop n=5000 times until the binary sequence S is traversed;
4) Complexity C N Normalization, for the binary sequence S, the final complexity calculation result is normalized, and the calculation method is as follows:
the method for judging the running state of the transformer by comparing the running state with the complexity threshold value set in the section comprises the following steps:
the threshold setting method is as follows:
the complexity threshold value of the vibration signal in the working condition A interval is the complexity average value of the historical vibration data and is marked as C Amean By analogy, the other 4 interval thresholds are noted as C Bmean 、C Cmean 、C Dmean And C Emean ；
The running state of the transformer is divided into four types of normal, attention, alarm and fault, and the specific judging running state rule is as follows:
1) When the absolute value of the difference between the calculated value of C and the belonging interval threshold value is larger than or equal to 0.5 times of the belonging interval threshold value, the current running state is judged to be attention;
2) When the absolute value of the difference between the calculated value of C and the affiliated section threshold value is larger than the affiliated section threshold value which is 1 time, judging the current running state as an alarm;
3) Within 24 hours, when the absolute value of the difference between the calculated value of C and the affiliated section threshold value is more than 1 time, judging that the current running state is a fault;
4) When any 2 or all of 1), 2) and 3) are satisfied, judging that the current running state is the state with the highest degree of abnormality, and sequentially carrying out normal, attention, alarm and fault on the order of the degree of abnormality from low to high;
5) And when any one of the above 1), 2) and 3) is not satisfied, determining that the current operation state is normal.
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